CN114186727B - Multi-cycle logistics network planning method and system - Google Patents
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Abstract
The invention relates to a multi-period logistics network planning method and a system, wherein the method comprises the following steps: acquiring service information in a logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information; preprocessing the service information to obtain model input data; planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a line plan and a vehicle plan; and determining a final logistics implementation planning scheme according to the logistics planning scheme. The invention can improve the planning efficiency under the condition of obtaining the global optimal solution of the network path planning.
Description
Technical Field
The invention relates to the field of logistics path planning, in particular to a multi-cycle logistics network planning method and system.
Background
In order to solve the network planning problem under a complex network structure, the existing network planning method either cannot obtain a global optimal solution or consumes a large amount of computing resources. For example, based on a shortest path algorithm, a route in each flow direction is found, then optimization is performed only for sorting times and transportation distances in a single flow direction each time, and since routes and route optimization in different flow directions are independent, the problem that the routes in different flow directions share resources is not considered, so that the problem that a route or a vehicle plan between flow directions with different transportation demands cannot meet an actual vehicle load or a minimum opening standard of the route exists in an actual floor implementation process of an optimized result. The solution based on each flow direction path is based on a candidate basic network structure, an enumeration method based on service constraint is adopted, feasible routes of all flow directions are exhausted, and the solution can acquire the required candidate feasible routes within the allowable time under a small-scale network. However, as the network scale increases, exhaustion of all the alternative routes in the flow direction consumes a large amount of computing resources, and even the problem that all the alternative routes in an actual scene cannot be obtained occurs, and the difficulty of solving a subsequent mathematical model due to the increase of the alternative routes is exponentially increased, and a feasible solution cannot be quickly found in a limited time. Even if the exhaustive scale of the route can be limited by some business rules, the global optimal solution cannot be obtained in engineering application, and the optimization space is reduced. Therefore, there is a need for a method that can find a globally optimal solution for network planning without consuming a large amount of computing resources.
Disclosure of Invention
The invention aims to provide a multi-period logistics network planning method and a multi-period logistics network planning system, which are used for improving planning efficiency under the condition of obtaining a global optimal solution for network path planning.
In order to achieve the purpose, the invention provides the following scheme:
a multi-cycle logistics network planning method comprises the following steps:
acquiring service information in a logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information;
preprocessing the service information to obtain model input data;
planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a line plan and a vehicle plan;
and determining a final logistics implementation planning scheme according to the logistics planning scheme.
Optionally, before the acquiring the service information in the logistics planning database, the method further includes:
acquiring service information in an operation database; the service information comprises line information, node information, vehicle information and demand flow direction and timeliness requirement information of a prediction system;
and synchronizing the line information, the node information, the vehicle information, the demand flow direction and the timeliness requirement information to the logistics planning database.
Optionally, the preprocessing the service information to obtain model input data specifically includes:
preprocessing the service information according to space-time nodes to obtain a space-time node set;
preprocessing the service information according to a time arc to obtain a time arc set;
preprocessing the service information according to a transportation arc to obtain a transportation arc set;
preprocessing the service information according to a demand flow direction to obtain a demand flow direction set;
and determining model input data according to the space-time node set, the time arc set, the transportation arc set and the demand flow direction set.
Optionally, the planning model comprises an objective function and constraints;
the expression of the objective function is:
wherein od is the demand flow set, transport _ arc is the transport arc set,
percentage of the flow of demand to i to the flow of cargo through the j arc, cost j For unit transportation cost, sort _ cost j The unit sorting cost is m is the upper limit of the demand flow direction i, and n is the upper limit of the transport arc j;
the expression of the constraint condition is as follows:
wherein,
demand flow to i flow to the percentage of the volume of the cargo passing through the j arc, demand i The quantity of goods to be distributed is required to flow to i in a demand flow direction, j is a transportation arc sent from a node d, i is the demand flow direction passing through the transportation arc j, node is a space-time node set, s is a demand flow direction starting node, t is a demand flow direction target node, o is a transportation arc starting node, d is a transportation arc target node, arc is a network arc set formed by the transportation arc and a time arc, capacity j For the loading capacity of the transport arc j, x j The frequency of the transport arc j, i.e., the number of vehicles used for the transport plan represented by the execution of the transport arc j.
Optionally, determining a final logistics implementation planning scheme according to the logistics planning scheme specifically includes:
judging whether the logistics planning scheme meets the operation index; if so, determining the logistics planning scheme as a final logistics implementation planning scheme; if not, updating the logistics planning database and the planning model by using the logistics planning scheme, and returning to the step of acquiring the service information in the logistics planning database.
A multi-cycle logistics network planning system, comprising:
the first acquisition module is used for acquiring service information in the logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information;
the preprocessing module is used for preprocessing the service information to obtain model input data;
the planning module is used for planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a line plan and a vehicle plan;
and the final logistics implementation planning scheme determining module is used for determining a final logistics implementation planning scheme according to the logistics planning scheme.
Optionally, the method further comprises:
the second acquisition module is used for acquiring the service information in the operation database; the service information comprises line information, node information, vehicle information and demand flow direction and timeliness requirement information of a prediction system;
and the synchronization module is used for synchronizing the line information, the node information, the vehicle information, the demand flow direction and the timeliness requirement information to the logistics planning database.
Optionally, the preprocessing module specifically includes:
the space-time node set determining unit is used for preprocessing the service information according to space-time nodes to obtain a space-time node set;
the time arc set determining unit is used for preprocessing the service information according to a time arc to obtain a time arc set;
the transportation arc set determining unit is used for preprocessing the service information according to the transportation arcs to obtain a transportation arc set;
a demand flow direction set determining unit, configured to preprocess the service information according to a demand flow direction to obtain a demand flow direction set;
and the model input data determining unit is used for determining model input data according to the space-time node set, the time arc set, the transportation arc set and the demand flow direction set.
Optionally, the planning model comprises an objective function and constraints;
the expression of the objective function is:
wherein od is the demand flow set, transport _ arc is the transport arc set,
percentage of the flow of demand to i to the flow of cargo through the arc of j, cost j For unit transportation cost, sort _ cost j Taking the unit sorting cost as a unit, wherein m is the upper limit of the demand flow direction i, and n is the upper limit of the transport arc j;
the expression of the constraint condition is as follows:
wherein,
demand flow to i flow to the percentage of the volume of the cargo passing through the j arc, demand i The quantity of goods to be distributed is required to flow to i in a demand flow direction, j is a transportation arc sent from a node d, i is the demand flow direction passing through the transportation arc j, node is a space-time node set, s is a demand flow direction starting node, t is a demand flow direction target node, o is a transportation arc starting node, d is a transportation arc target node, arc is a network arc set formed by the transportation arc and a time arc, capacity j For the loading capacity of the transport arc j, x j The frequency of the transport arc j, i.e., the number of vehicles used for the transport plan represented by the execution of the transport arc j.
Optionally, determining a final logistics implementation planning scheme according to the logistics planning scheme specifically includes:
judging whether the logistics planning scheme meets the operation index; if so, determining the logistics planning scheme as a final logistics implementation planning scheme; if not, updating the logistics planning database and the planning model by using the logistics planning scheme, and returning to the step of acquiring the service information in the logistics planning database.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention obtains the business information in the logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information; preprocessing the service information to obtain model input data; planning by using a planning model according to the model input data to obtain a logistics planning scheme; and determining a final logistics implementation planning scheme according to the logistics planning scheme. Compared with the global network planning based on the path, the method omits the process of exhaustive routing, simplifies the data processing process and improves the calculation efficiency of the planning model. And routing deletion is not required to be performed based on a business rule, so that the final logistics implementation planning scheme is a global optimal solution under the planning constraint.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a multi-cycle logistics network planning method provided by the present invention;
FIG. 2 is a schematic diagram of a network planning scheme;
FIG. 3 is a schematic diagram of a multi-cycle logistics network planning method provided by the present invention;
fig. 4 is a schematic structural diagram of a multi-cycle logistics network planning system provided by the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a multi-period logistics network planning method and a multi-period logistics network planning system, which are used for improving planning efficiency under the condition of obtaining a global optimal solution for network path planning.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Network planning is an important means for guaranteeing the efficient operation of a logistics network, and the aim of the network planning is to complete a transportation task in a specific flow direction by reasonably arranging lines, transportation capacity and a routing plan. There are two basic concepts in the network planning problem, namely the basic network structure and the traffic demand flow. Fig. 2(a) is a network planning problem, fig. 2(b) is a feasible network planning scheme, and as shown in the left side of the dotted line of fig. 2(a), beijing, west ann, wuhan, and shenzhen can form a simple network, candidate lines can be opened between any two points, and all the openable candidate lines and nodes form a basic network structure. As shown in fig. 2(a) on the right side of the dotted line, the flow directions of < beijing-shenzhen, 50 tons >, < beijing-wuhan, 20 tons >, < wuhan-shenzhen, 30 tons > and < xi' an-shenzhen, 10 tons > are 4 transportation requirements. How to select a proper line combination with the aim of minimizing cost as a target and complete 4 distribution tasks in the flow direction of the demand on the premise of meeting practical constraints belongs to a classical scene in network planning. As shown in fig. 2(b), many possible line combining schemes can be found. In fig. 2(b), solution 1 and solution 2 are both feasible network structures, but the combination of the lines of solution 2 is a challenging matter because of fewer lines and shorter total transportation mileage, and the feasible solutions of the actual network are in hundreds of millions, and the optimal network structure is selected from the massive feasible solutions.
As shown in fig. 1, the method for planning a multi-cycle logistics network provided by the present invention includes:
step 101: acquiring service information in a logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information.
Step 102: and preprocessing the service information to obtain model input data. Step 102, specifically comprising: preprocessing the service information according to space-time nodes to obtain a space-time node set; preprocessing the service information according to a time arc to obtain a time arc set; preprocessing the service information according to a transport arc to obtain a transport arc set; preprocessing the service information according to a demand flow direction to obtain a demand flow direction set; and determining model input data according to the space-time node set, the time arc set, the transportation arc set and the demand flow direction set. The time arcs represent arcs connecting two different time nodes on the same space node, the arc length represents a period of time on the space node, and the capacity of the arcs represents the sorting capacity in the period of time. The transport arc represents an arc connecting two time nodes at different spatial nodes, the length of the arc is transferred from one spatial node to another spatial node, and the capacity of the arc represents the loading capacity of a vehicle (such as a vehicle or an airplane) represented by the transport arc.
Step 103: planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a route plan and a vehicle plan. The planning model in the invention is an arc-flow based network flow model (arc-flow model).
Wherein the planning model comprises an objective function and constraints;
the expression of the objective function is:
wherein od is the demand flow set, transport _ arc is the transport arc set,
percentage of the flow of demand to i to the flow of cargo through the j arc, cost j For unit transportation cost, sort _ cost j Taking the unit sorting cost as a unit, wherein m is the upper limit of the demand flow direction i, and n is the upper limit of the transport arc j;
the expression of the constraint condition is as follows:
wherein,
demand flow to i flow to the percentage of the volume of the cargo passing through the j arc, demand i The quantity of goods to be distributed is required to flow to i in a demand flow direction, j is a transportation arc sent from a node d, i is the demand flow direction passing through the transportation arc j, node is a space-time node set, s is a demand flow direction starting node, t is a demand flow direction target node, o is a transportation arc starting node, d is a transportation arc target node, arc is a network arc set formed by the transportation arc and a time arc, capacity j For the loading capacity of the transport arc j, x j The frequency of the transport arc j, i.e., the number of vehicles used for the transport plan represented by the execution of the transport arc j.
Step 104: and determining a final logistics implementation planning scheme according to the logistics planning scheme.
judging whether the logistics planning scheme meets the operation index or not; if so, determining the logistics planning scheme as a final logistics implementation planning scheme; if not, updating the logistics planning database and the planning model by using the logistics planning scheme, and returning to the step 101. And updating the planning model by using the logistics planning scheme, namely, increasing constraint conditions in the planning model by using the logistics planning scheme so as to enable the planning model to be more practical. The operation indexes refer to key operation indexes output by a simulation model, such as whether the sorting amount of a transfer station under a new scheme exceeds a design peak value, whether the loading and unloading tasks of each platform cause too many unloading screens to appear in a queuing field due to the new scheme, and the like, which are closer to reality and more detailed.
In practical applications, before step 101, the method further includes: acquiring service information in an operation database; the service information comprises line information, node information, vehicle information and demand flow direction and timeliness requirement information of a prediction system; and synchronizing the line information, the node information, the vehicle information, the demand flow direction and the timeliness requirement information to the logistics planning database.
As shown in fig. 3, the method provided by the present invention is specifically divided into six modules, namely a planning database, data preprocessing, an arc-flow model, scheme checking, an operation database, and planning requirement updating. The operation system is used for supporting the information management system of the normal operation of the enterprise logistics network, such as waybill management, route management, sorting yard management system and the like, the information management system manages data related to logistics basic network and customer requirements, such as lines, cargo distribution requirements, site information and the like, the data are regularly precipitated to form an operation database, and the operation condition of the scheme in practice is recorded. And the planning database is used for providing basic service data for data preprocessing of the arc-flow model, and the data of the planning database records the information related to the current latest state of the network and the predicted distribution demand in the operation system. The input data is mainly provided for the data preprocessing module. And the data preprocessing module processes the service processing data in the planning database into a standard data set which can be identified by the arc-flow model. The Arc-flow model is responsible for exporting a network solution that meets all delivery requirements. And the scheme checking module performs simulation evaluation on the cost, the field, the line and the like of the scheme under different parts levels in a mode of being more fit with the actual state of the service, and verifies the robustness of the scheme under different scenes. And updating the constraint of the planning requirement according to the evaluation result, and continuously performing iterative optimization according to the flow of the figure 3 until a certain condition is met.
Step 1: various alternative lines, nodes and vehicle network basic information are synchronized from an operation database by a drawing module of a planning database, and service requirement information such as a requirement flow direction and a customer aging requirement is acquired from an operation list or a prediction system. The data set is deposited into a planning database as a basic data source for solving the network planning problem.
Step 2: and converting the service information of the planning database into a standard model input data format required by the arc-flow model by using a data preprocessing module.
And step 3: and outputting solutions such as optimized line combination, flow direction routing, line planning and the like by an arc-flow model based on a network structure and service distribution requirements required by planning.
And 4, step 4: because the arc-flow model is used for improving the output efficiency of the scheme, practical problems need to be abstracted, and some minor operation rules, such as departure rules and more detailed information of goods flowing in the sorting system, are eliminated. Therefore, before the solution is synchronized to the operating system for landing, the solution inspection module needs to evaluate the landing possibility and the reasonableness of the solution based on simulation or manual experience. For example, a network simulation model containing more operation details (such as departure rules, sorting processes in a transit yard, etc.) can be established based on a discrete event simulation technology. The simulation model receives network solutions (generally comprising lines, vehicles, routing plans, and the like) from the arc-flow model optimization outputs, and the schemes output by the arc-flow model are evaluated in a more realistic digital twin world. And observing whether other operation problems occur, such as whether overlong vehicle loading and unloading queues occur in certain transfer stations, supersaturation caused by insufficient sorting capacity occurs, and the like, if the scheme is feasible, sorting, lines and vehicle plans output by the arc-flow model can be synchronized to an operation database, an operation system generates distribution routes in each demand flow direction and sorting plans of corresponding nodes based on the optimized lines, and prepares transportation capacity resources such as vehicles, airplanes and railways in advance according to the vehicle plans planned by the arc-flow model. If the scheme is not reasonable, certain important constraints are updated based on the evaluation result, for example, whether the supersaturated sorting can be subjected to capacity transformation or not is evaluated, and the original sorting capacity is expanded. And generating a new planning demand, updating the information in the planning database by the planning demand updating module according to the planning demand data generated by evaluation, generating new model input data by the data preprocessing module again, and iteratively outputting a final landing scheme.
And updating some important constraints based on the evaluation result, specifically, updating the constraints firstly, then updating to the arc-flow model for the next iteration, and outputting a new optimization scheme. For example, after evaluating the network solution outputted in the previous step, it is found that some transfer platforms and sorting capacity are insufficient, and in the next optimization step, constraints for limiting the transfer departure and arrival are increased. And then outputs the next new version of the network solution.
The information in the planning database is updated, in particular, for example, in the context of route information of the planning database. Before optimization, according to the network structure display synchronized by the operation database, 1000 lines are needed to ensure that all transportation requirements can be delivered. After the optimization of the method, 50 lines need to be deleted from the original line, 10 lines need to be added, the distribution requirement can be met only by 960 lines, and the cost is lower. The optimized line information is synchronized to a planning database, then manual or simulation verification evaluation is carried out, iteration is carried out until the scheme can be executed, and then the scheme is synchronized to an operation database from the planning database, so that a new line, sorting and vehicle arrangement scheme is formed.
And iteratively outputting a final touchable scheme, specifically an Arc-flow model output solution, checking the feasibility of the model verification scheme by the scheme, generating new service constraints, adding the new constraints into the Arc-flow model, outputting a new network solution, and verifying the feasibility of the scheme again. Such as iteration, until the final output is an optimization scheme that can be performed on the ground.
In fig. 3, since the data preprocessing module and the arc-flow model module are two key modules in the flow and are what this patent requires to protect, the details of the two modules will be described in detail below. In the data preprocessing module part, four parts including time nodes, time arcs, transport arcs and demand flow direction processing are included.
Tables 1-4 show the details of the model standard input data after the business data in the planning database is processed by the data preprocessing module. This data will be referred to as a model recognizable by the arc-flow model. Table 1 is the time nodes in the spatio-temporal network diagram, table 2 is the time arcs in the spatio-temporal network diagram, table 3 is the transport arcs in the spatio-temporal network diagram, and table 4 is the demand flow direction in the spatio-temporal network diagram. It is well known that networks are generally made up of nodes and arcs. Tables 1-3 construct spatio-temporal network diagrams required for the arc-flow model. Where table 1 is a network node in a spatio-temporal network diagram. Tables 2 and 3 show the network arcs in the diagram, which are used for connecting the network nodes in table 1, so that the nodes in table 1 are connected into a fully-connected spatio-temporal network. Table 2 shows connection arcs between any two adjacent time nodes on the same spatial node in table 1, and table 3 shows network arcs connecting different spatial nodes in table 1 and different network nodes. The spatio-temporal network map is a mapping of transit shifts, line data and transport distribution demand data in an actual network. Therefore, the real two-dimensional space-time network can be reduced into a one-dimensional network. The example data of each table will be explained in detail below.
TABLE 1 time nodes in spatio-temporal network diagrams
| Key value | Time node | Date of day | Time | Space node |
| TN001 | 1-0700-Beijing | 1 | 07:00 | Beijing |
| TN002 | 1-0830-Beijing | 1 | 08:30 | Beijing |
| TN003 | 1-1100-Beijing | 1 | 11:00 | Beijing |
| TN004 | 1-0700-Shenzhen | 1 | 07:00 | Shenzhen (Shenzhen medicine) |
| TN005 | 2-1500-Wuhan | 2 | 15:00 | (Wuhan) |
| TN006 | 3-2000 Shenzhen | 3 | 20:00 | Shenzhen (Shenzhen medicine) |
Table 1 shows a standard data format of a time node, where a key value represents a unique identifier, the time node represents time information representing each space node, and 3 rows of data in which the space node in the table is beijing are used as an example. TN001, TN002, and TN003 represent the presence of three time nodes 07:00, 08:30, and 11:00, respectively, on the Beijing node. The node represents a key time node in operation such as departure of a route, arrival of a route, start of a shift, end of a shift and the like.
TABLE 2 time arcs in spatio-temporal network diagrams
Table 2 represents virtual time arcs connecting two adjacent time nodes on the same space node, and TA001 and TA002 represent 2 virtual time arcs connecting three time nodes, 07:00, 08:00, and 11:00, on the beijing node. For example, TA001 in Table 2 represents the arc of two time nodes TN001 and TN002 in the table. It is noted that the time arcs connect different time nodes of two same spatial nodes. For example, TN003 and TN004 in table 1 cannot form a time arc because they are time nodes on two different spatial nodes, namely beijing and shenzhen. It should be noted that the processing capacity of the time arc represents the sorting capacity or the unloading capacity at a given time.
TABLE 3 traffic arcs in spatio-temporal network diagrams
Table 3 represents the time arcs connecting time nodes on different spatial nodes, which we call transport arcs. Which represents the lines present in the candidate network structure. As shown in table 3, TP001 and TP002 represent two lines connecting beijing-wuhan and wuhan-shenzhen, respectively, and taking TP001 as an example, the transport arc connects two network nodes TN001 and TN005 in table 2. The fact that a line sent from Beijing to Wuhan exists in the actual logistics network shows that the departure time of the line in Beijing is 07:00 of 1 st day, and the arrival time of the line in Wuhan is 15:00 of 2 nd day. This capacity represents the vehicle capacity of a given line, which represents the 150 ton available capacity. The cost of the transport arc is the unit cost of the line.
TABLE 4 flow of demand in spatio-temporal network diagrams
Table 4 shows that the transportation requirements flow to the standard data structure in the network, and DO001 and DO002 represent two distribution requirements of Beijing-Shenzhen and Wuhan-Shenzhen. Where DO001 denotes a 10 ton supply at the TN001 node and a 20 ton demand at the TN006 node in the spatio-temporal network diagram. Representing that 07:00 Beijing on day 1 in the actual network has a batch of goods sent to Shenzhen and needs to reach Shenzhen 20:00 before day 3.
All model standard input data required for establishing the arc-flow model are obtained through the data preprocessing, the objective function of the arc-flow model is,
the objective function represents minimizing network transportation costs.
The constraint condition is
In the first constraint, d ≠ s & d ≠ t denotes traffic balancing for arbitrary non-demand originating and destination nodes.
In the second constraint, d-s means that for any demand originating node, the supply must be delivered in its entirety.
In the third constraint, d-t means that for any demand destination node, the demands must all be satisfied.
The fourth constraint indicates that for any line, the line capacity-full constraint must be met.
The node is a node set formed by space-time nodes, and the value of the node set is a set shown in a key value field in table 1. Including TN001, TN002, TN003, and the like.
time_arc <i，j> Is a set of arcs consisting of time arcs, where i, j ∈ node, time _ arc <i，j> The value of the set is the set shown in the key value field in Table 2, and as shown in the data example in Table 2, TA001 and TA002 in Table 2 form time _ arc <i，j> A collection of (a). Wherein i, j respectively represent time _ arc <i，j> An originating node and a destination node.
transport_arc <i，j> Is a set of arcs consisting of a transport arc, where i, j ∈ node. transport _ arc <i，j> The values of the set are the set shown in the key value field in table 3, and as shown in the data example in table 3, TP001 and TP002 in table 2 constitute the set of transport _ arc. Wherein i, j respectively represent transport _ arc <i，j> The originating node and the destination node.
arc <i，j> Is a network arc set composed of a transport arc and a time arc, wherein, arc <i，j> ＝transport_arc <i，j> ∪time_arc <i，j> And (5) transporting the arc.
od <i，j> For a set of demand flow directions, od, consisting of spatio-temporal nodes <i，j> The values of the set are the set shown in the key value field in Table 4, and include DO001 and DO 002. Where i is the originating node and j is the destination node. And i, j ∈ node.
cost i For a unit shipping cost, i ∈ transport _ arc. The values of this parameter are shown in the cost field of Table 3, as cost TP001 The cost of the transport arc TP001 is represented as 1920 dollars per trip.
capacity i : if i ∈ transport _ arc, then the line is full, the value of this parameter is shown in Table 3 as the load capability field, as capacity TP001 150, the maximum load capacity of TP001 is 150. If i ∈ time _ arc, then for the sorting capacity of that time period, the value of this parameter is shown in the processing capacity field in Table 3, as capacity TA001 1500, TA001 represents 07:00-08:30 up to a maximum sorting capacity of 1500 squares.
demand i The amount of goods to be delivered for demand flow i ∈ od, the value of which is shown in Table 4 as demand, e.g. demand DO001 The flow rate to DO001 is 20, indicating that the amount of goods to be delivered is 20.
The arc-flow model uses a solver such as scip and cpelx to carry out mathematical modeling and solving on the expression.
The invention firstly designs a new arc-flow model for network planning, converts a multi-period two-dimensional space-time network into a one-dimensional space-time network, reduces the waste of computing resources caused by exhaustive routing in practical application, and takes the optimal solution as the global optimal solution. Secondly, a new method for solving the problem of actual network planning is provided, and the functions of a planning center with real-time optimization can be provided for a complex network by combining an optimization system, a simulation system and an actual service system.
As shown in fig. 4, the system for planning a multi-cycle logistics network provided by the present invention is characterized by comprising:
a first obtaining module 201, configured to obtain service information in a logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information.
And the preprocessing module 202 is configured to preprocess the service information to obtain model input data.
The planning module 203 is used for planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a route plan and a vehicle plan.
And a final logistics implementation planning scheme determining module 204, configured to determine a final logistics implementation planning scheme according to the logistics planning scheme.
In practical application, the method further comprises the following steps:
the second acquisition module is used for acquiring the service information in the operation database; the service information comprises line information, node information, vehicle information and demand flow and timeliness requirement information of the prediction system.
And the synchronization module is used for synchronizing the line information, the node information, the vehicle information, the demand flow direction and the timeliness requirement information to the logistics planning database.
In practical applications, the preprocessing module 202 specifically includes: the space-time node set determining unit is used for preprocessing the service information according to space-time nodes to obtain a space-time node set; the time arc set determining unit is used for preprocessing the service information according to a time arc to obtain a time arc set; the transportation arc set determining unit is used for preprocessing the service information according to the transportation arcs to obtain a transportation arc set;
a demand flow direction set determining unit, configured to preprocess the service information according to a demand flow direction to obtain a demand flow direction set; and the model input data determining unit is used for determining model input data according to the space-time node set, the time arc set, the transportation arc set and the demand flow direction set.
In practical application, the planning model comprises an objective function and a constraint condition;
the expression of the objective function is:
wherein od is demand flow set, transport _ arc is transport arc set, and demand is set i The amount of goods that need to be delivered for demand flow to i,
percentage of the flow of demand to i to the flow of cargo through the j arc, cost j For unit transportation cost, sort _ cost j For a unit sorting cost, m is the upper limit of the demand flow direction i, and n is the upper limit of the transport arc j；
The expression of the constraint condition is as follows:
wherein,
flow into percentage of the traffic passing through the j section arc for demand flow direction i, j is the transport arc sent from node d, i is the demand flow direction passing through the transport arc j, node is the space-time node set, s is the demand flow direction originating node, t is the demand flow direction destination node, o is the transport arc originating node, d is the transport arc destination node, arc is the network arc set formed by the transport arc and the time arc, capacity j For the loading capacity of the transport arc j, x j The frequency of the transport arc j, the number of vehicles used for executing the transport plan represented by the transport arc j.
In practical applications, the final logistics implementation planning scheme determining module 204 specifically includes:
the judging unit is used for judging whether the logistics planning scheme meets the operation index; if so, determining the logistics planning scheme as a final logistics implementation planning scheme; if not, updating the logistics planning database and the planning model by using the logistics planning scheme, and returning to the step of acquiring the service information in the logistics planning database.
The invention abstracts a two-dimensional spatio-temporal network into a one-dimensional spatio-temporal network comprising periods, spatial nodes and temporal nodes. Abstracting attributes such as duration, processing capacity, parking places, sorting cost and transportation cost into various attributes of arcs, and acquiring network planning solutions such as lines, paths, sorting and vehicle plans of the complex network based on solvers such as scip, cplex or gurobi. And then, verifying and evaluating the network solution acquired based on the arc-flow model by adopting a simulation verification and evaluation mode to acquire unreasonable lines, sorting and vehicle plans in the solution, adding the unreasonable solution into arc-flow model constraints, and outputting the iterated network solution based on the updated arc-flow model until the solution can meet the actual application requirements. Compared with the global network planning based on the path, the scheme omits the process of exhaustive routing, simplifies the data processing process and improves the calculation efficiency of models of other schemes. And routing deletion is not required to be carried out based on a business rule, so that the solved solution is the global optimal solution under the planning constraint.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A multi-cycle logistics network planning method is characterized by comprising the following steps:
acquiring service information in a logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information;
preprocessing the service information to obtain model input data;
planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a line plan and a vehicle plan;
determining a final logistics implementation planning scheme according to the logistics planning scheme;
the planning model comprises an objective function and a constraint condition;
the expression of the objective function is:
wherein od is the demand flow set, transport _ arc is the transport arc set,
percentage of the flow of demand to i to the flow of cargo through the j arc, cost j For unit transportation cost, sort _ cost j Taking the unit sorting cost as a unit, wherein m is the upper limit of the demand flow direction i, and n is the upper limit of the transport arc j;
the expression of the constraint condition is as follows:
wherein,
demand flow to i flow to the percentage of the volume of the cargo passing through the j arc, demand i The quantity of goods to be distributed is required to flow to i in a demand flow direction, j is a transportation arc sent from a node d, i is the demand flow direction passing through the transportation arc j, node is a space-time node set, s is a demand flow direction starting node, t is a demand flow direction target node, o is a transportation arc starting node, d is a transportation arc target node, arc is a network arc set formed by the transportation arc and a time arc, capacity j For the loading capacity of the transport arc j, x j The frequency of the transport arc j, i.e., the number of vehicles used for the transport plan represented by the execution of the transport arc j.
2. The multi-cycle logistics network planning method of claim 1, further comprising, before the obtaining the service information in the logistics planning database:
acquiring service information in an operation database; the service information comprises line information, node information, vehicle information and demand flow direction and timeliness requirement information of a prediction system;
and synchronizing the line information, the node information, the vehicle information, the demand flow direction and the timeliness requirement information to the logistics planning database.
3. The multi-cycle logistics network planning method of claim 1, wherein the preprocessing the service information to obtain model input data specifically comprises:
preprocessing the service information according to space-time nodes to obtain a space-time node set;
preprocessing the service information according to a time arc to obtain a time arc set;
preprocessing the service information according to a transport arc to obtain a transport arc set;
preprocessing the service information according to a demand flow direction to obtain a demand flow direction set;
and determining model input data according to the space-time node set, the time arc set, the transportation arc set and the demand flow direction set.
4. The multi-cycle logistics network planning method of claim 1, wherein the determining a final logistics implementation planning scheme according to the logistics planning scheme specifically comprises:
judging whether the logistics planning scheme meets the operation index; if so, determining the logistics planning scheme as a final logistics implementation planning scheme; if not, updating the logistics planning database and the planning model by using the logistics planning scheme, and returning to the step of acquiring the service information in the logistics planning database.
5. A multi-cycle logistics network planning system, comprising:
the first acquisition module is used for acquiring service information in the logistics planning database; the service information comprises a demand flow direction, node information, line information, timeliness requirement information and vehicle information;
the preprocessing module is used for preprocessing the service information to obtain model input data;
the planning module is used for planning by using a planning model according to the model input data to obtain a logistics planning scheme; the logistics planning scheme comprises an aging plan, a sorting plan, a line plan and a vehicle plan;
the final logistics implementation planning scheme determining module is used for determining a final logistics implementation planning scheme according to the logistics planning scheme;
the planning model comprises an objective function and a constraint condition;
the expression of the objective function is:
wherein od is the demand flow set, transport _ arc is the transport arc set,
percentage of the flow of demand to i to the flow of cargo through the j arc, cost j For unit transportation cost, sort _ cost j Taking the unit sorting cost as a unit, wherein m is the upper limit of the demand flow direction i, and n is the upper limit of the transport arc j;
the expression of the constraint condition is as follows:
wherein,
demand flow to i flow to the percentage of the volume of the cargo passing through the j arc, demand i The amount of goods to be delivered for a demand flow direction i, j is a traffic arc issued from a node d, i is the demand flow direction passing through the traffic arc j, node is a space-time node set, s is a demand flow direction originating node, t is a demand flow direction destination node, o is a traffic arc originating node, and d is a traffic arc destination nodePoint, arc, is a set of network arcs, capacity, formed by a transport arc and a time arc j For the loading capacity of the transport arc j, x j The frequency of the transport arc j, i.e., the number of vehicles used for the transport plan represented by the execution of the transport arc j.
6. The multi-cycle logistics network planning system of claim 5, further comprising:
the second acquisition module is used for acquiring the service information in the operation database; the service information comprises line information, node information, vehicle information and demand flow direction and timeliness requirement information of a prediction system;
and the synchronization module is used for synchronizing the line information, the node information, the vehicle information, the demand flow direction and the timeliness requirement information to the logistics planning database.
7. The multi-cycle logistics network planning system of claim 5, wherein the preprocessing module specifically comprises:
the space-time node set determining unit is used for preprocessing the service information according to space-time nodes to obtain a space-time node set;
the time arc set determining unit is used for preprocessing the service information according to a time arc to obtain a time arc set;
the transportation arc set determining unit is used for preprocessing the service information according to the transportation arcs to obtain a transportation arc set;
a demand flow direction set determining unit, configured to preprocess the service information according to a demand flow direction to obtain a demand flow direction set;
and the model input data determining unit is used for determining model input data according to the space-time node set, the time arc set, the transportation arc set and the demand flow direction set.
8. The multi-cycle logistics network planning system of claim 5, wherein the final logistics implementation planning scheme determination module specifically comprises:
the judging unit is used for judging whether the logistics planning scheme meets the operation index; if so, determining the logistics planning scheme as a final logistics implementation planning scheme; if not, updating the logistics planning database and the planning model by using the logistics planning scheme, and returning to the step of acquiring the service information in the logistics planning database.
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